Predicting daily pore water pressure in embankment dam: Empowering Machine Learning-based modeling.
Dam safety
Heimer dam
Hydrostatic seasonal time
Machine learning models
Pore water pressure
Journal
Environmental science and pollution research international
ISSN: 1614-7499
Titre abrégé: Environ Sci Pollut Res Int
Pays: Germany
ID NLM: 9441769
Informations de publication
Date de publication:
Jul 2022
Jul 2022
Historique:
received:
30
09
2021
accepted:
04
01
2022
revised:
02
01
2022
pubmed:
20
2
2022
medline:
29
6
2022
entrez:
19
2
2022
Statut:
ppublish
Résumé
Dam safety assessment is important to implement the appropriate measures to avoid a dam break disaster as part of the water reservoirs management process. Prediction-based approaches are valuable to compare the actual measurements with the simulated values to proactively detect anomalies. However, the application of the conventional hydrostatic seasonal time (HST) has some limitations related to an instantaneous response of the dam to environmental factors, which can lead to inaccurate prediction and interpretation, especially for daily measurements. Besides, the generalization ability (GA) of these models is not analyzed enough despite its crucial importance in selecting the appropriate models. In this study, the multiple linear regression (MLR), artificial neural network (ANN), support vector regression (SVR), and adaptive boosting (AdaBoost) models with nonlinear autoregressive exogenous (NARX) inputs are proposed to incorporate the response delay of the dam to the hydraulic load. Thus, these models were evaluated and compared with the HST model for predicting the daily pore water pressure in an embankment dam. Moreover, we proposed a classification method of the models into four categories, namely perfect, excellent, good, and poor according to the GA. Results show that, except for the AdaBoost, the other ML models outperformed the traditional statistical approach (HST) in terms of prediction accuracy as well as the GA. Overall, the study results provide new insights in enhancing the monitoring processes and dam safeties by detecting the anomalies early through the comparison of the measurements and simulated results produced by the best-fitted models from the confidence interval (CI) perspective.
Identifiants
pubmed: 35181857
doi: 10.1007/s11356-022-18559-7
pii: 10.1007/s11356-022-18559-7
doi:
Substances chimiques
Water
059QF0KO0R
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
47382-47398Informations de copyright
© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.
Références
Aggarwal CC (2015) Data mining, Springer International Publishing. https://doi.org/10.1007/978-3-319-14142-8
W Al-Fares 2011 Contribution of the geophysical methods in characterizing the water leakage in Afamia B dam, Syria J Appl Geophys 75 464 471 https://doi.org/10.1016/j.jappgeo.2011.07.014
doi: 10.1016/j.jappgeo.2011.07.014
NH Al-Saigh ZS Mohammed MS Dahham 1994 Detection of water leakage from dams by self-potential method Eng. Geol. 37 115 121 https://doi.org/10.1016/0013-7952(94)90046-9
doi: 10.1016/0013-7952(94)90046-9
S Arlot A Celisse 2010 A survey of cross-validation procedures for model selection Stat Surv 4 40 79
doi: 10.1214/09-SS054
Bear J (2013) Dynamics of fluids in porous media. Courier Corporation
A Belmokre MK Mihoubi D Santillan 2019 Seepage and dam deformation analyses with statistical models: support vector regression machine and random forest Procedia Struct Integr 17 698 703 https://doi.org/10.1016/j.prostr.2019.08.093
doi: 10.1016/j.prostr.2019.08.093
Beskhyroun S, Wegner LD, Sparling BF (2011) Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches. Struct Control Heal Monit n/a-n/a. https://doi.org/10.1002/stc
Bishop CM (2006) Pattern recognition and machine learning. springer
Bonaccorso G (2018) Machine learning algorithms: popular algorithms for data science and machine learning. Packt Publishing Ltd
S Bonelli H Félix 2001 Delayed response analysis of temperature effect. 6th ICOLD Benchmark Work Numer Anal Dams 2001 1 9
Carrere A, Noret-Duchêne C (2001) Interpretation of an arch dam behaviour using enhanced statistical models, in: Proceedings of the Sixth ICOLD Benchmark Workshop on Numerical Analysis of Dams, Salzburg, Austria
C Chen W He H Zhou Y Xue M Zhu 2020 A comparative study among machine learning and numerical models for simulating groundwater dynamics in the Heihe River Basin, northwestern China Sci Rep 10 1 13 https://doi.org/10.1038/s41598-020-60698-9
doi: 10.1038/s41598-020-60698-9
S Chen C Gu C Lin MA Hariri-Ardebili 2021 Prediction of arch dam deformation via correlated multi-target stacking Appl Math Model 91 1175 1193 https://doi.org/10.1016/j.apm.2020.10.028
doi: 10.1016/j.apm.2020.10.028
S Chen C Gu C Lin Y Wang MA Hariri-Ardebili 2020 Prediction, monitoring, and interpretation of dam leakage flow via adaptative kernel extreme learning machine Meas J Int Meas Confed 166 108161 https://doi.org/10.1016/j.measurement.2020.108161
doi: 10.1016/j.measurement.2020.108161
L Chouinard V Roy 2006 Performance of statistical models for dam Jt Int Conf Comput Decis Mak Civ Build Eng 2211 199 207
CW Dawson R Wilby 1998 Une approche de la modélisation pluie-deblt par ies réseaux neuronaux artificiels Hydrol Sci J 43 47 66 https://doi.org/10.1080/02626669809492102
doi: 10.1080/02626669809492102
M Granrut de A Simon D Dias 2019 Artificial neural networks for the interpretation of piezometric levels at the rock-concrete interface of arch dams Eng Struct 178 616 634 https://doi.org/10.1016/j.engstruct.2018.10.033
doi: 10.1016/j.engstruct.2018.10.033
A Desideri E Fontanella L Pagano 2013 Pore water pressure distribution for use in stability analyses of earth dams. Landslide Sci. Pract. Risk Assessment Manag Mitig 6 149 153 https://doi.org/10.1007/978-3-642-31319-6_21
doi: 10.1007/978-3-642-31319-6_21
A Bilali El A Taleb 2020 Prediction of irrigation water quality parameters using machine learning models in a semi-arid environment J Saudi Soc Agric Sci 19 439 451 https://doi.org/10.1016/j.jssas.2020.08.001
doi: 10.1016/j.jssas.2020.08.001
A Bilali El A Taleb M Abdellah Y Brouziyne 2021 An integrated approach based on Gaussian noises-based data augmentation method and AdaBoost model to predict faecal coliforms in rivers with small dataset J Hydrol 599 126510 https://doi.org/10.1016/j.jhydrol.2021.126510
doi: 10.1016/j.jhydrol.2021.126510
A Bilali El A Taleb I Boutahri 2021 Application of HEC-RAS and HEC-LifeSim models for flood risk assessment J Appl Water Eng Res 9 1 16 https://doi.org/10.1080/23249676.2021.1908183
doi: 10.1080/23249676.2021.1908183
El Bilali A, Taleb A, Brouziyne Y (2020) Groundwater quality forecasting using machine learning algorithms for irrigation purposes. Agric Water Manag 106625. https://doi.org/10.1016/j.agwat.2020.106625
A Bilali El A Taleb A Nafii B Alabjah 2021 Prediction of sodium adsorption ratio and chloride concentration in a coastal aquifer under seawater intrusion using machine learning models Environ Technol Innov 23 101641 https://doi.org/10.1016/j.eti.2021.101641
doi: 10.1016/j.eti.2021.101641
Ferry S, Willm G (1958) Méthodes d’analyse et de surveillance des déplacements observés par le moyen de pendules dans les barrages. In: VIth International Congress on Large Dams. pp. 1179–1201
WD Fisher TK Camp VV Krzhizhanovskaya 2017 Anomaly detection in earth dam and levee passive seismic data using support vector machines and automatic feature selection J Comput Sci 20 143 153 https://doi.org/10.1016/j.jocs.2016.11.016
doi: 10.1016/j.jocs.2016.11.016
Y Freund RE Schapire 1997 A decision-theoretic generalization of on-line learning and an application to boosting J Comput Syst Sci 55 119 139 https://doi.org/10.1006/jcss.1997.1504
doi: 10.1006/jcss.1997.1504
Freund Y, Schapire RE (1996) Experiments with a new boosting algorithm, in: Icml. Citeseer, pp. 148–156
S Gamse WH Zhou F Tan KV Yuen M Oberguggenberger 2018 Hydrostatic-season-time model updating using Bayesian model class selection Reliab Eng Syst Saf 169 40 50 https://doi.org/10.1016/j.ress.2017.07.018
doi: 10.1016/j.ress.2017.07.018
S Ghosh 2010 SVM-PGSL coupled approach for statistical downscaling to predict rainfall from GCM output J Geophys Res Atmos 115 1 18 https://doi.org/10.1029/2009JD013548
doi: 10.1029/2009JD013548
X Guo J Baroth D Dias A Simon 2018 An analytical model for the monitoring of pore water pressure inside embankment dams Eng Struct 160 356 365 https://doi.org/10.1016/j.engstruct.2018.01.054
doi: 10.1016/j.engstruct.2018.01.054
MA Hariri-Ardebili F Pourkamali-Anaraki 2018 Simplified reliability analysis of multi hazard risk in gravity dams via machine learning techniques Arch Civ Mech Eng 18 592 610 https://doi.org/10.1016/j.acme.2017.09.003
doi: 10.1016/j.acme.2017.09.003
Hastie T, Tibshirani R, Friedman J (2009) The elements of statistical learning: data mining, inference, and prediction. Springer Science & Business Media
Johansson S (1997) Seepage monitoring in embankment dams
IS Jung M Berges JH Garrett B Poczos 2015 Exploration and evaluation of AR, MPCA and KL anomaly detection techniques to embankment dam piezometer data Adv Eng Informatics 29 902 917 https://doi.org/10.1016/j.aei.2015.10.002
doi: 10.1016/j.aei.2015.10.002
F Kang J Li J Dai 2019 Prediction of long-term temperature effect in structural health monitoring of concrete dams using support vector machines with Jaya optimizer and salp swarm algorithms Adv Eng Softw 131 60 76 https://doi.org/10.1016/j.advengsoft.2019.03.003
doi: 10.1016/j.advengsoft.2019.03.003
F Kang J Liu J Li S Li 2017 Concrete dam deformation prediction model for health monitoring based on extreme learning machine Struct Control Heal Monit 24 1 11 https://doi.org/10.1002/stc.1997
doi: 10.1002/stc.1997
Kubat, M., 2017. An introduction to machine learning, Springer International Publishing. https://doi.org/10.1007/978-3-319-63913-0
M Kuhn K Johnson 2019 Feature engineering and selection: a practical approach for predictive models CRC Press https://doi.org/10.1201/9781315108230
doi: 10.1201/9781315108230
B Li J Yang D Hu 2020 Dam monitoring data analysis methods: a literature review Struct Control Heal Monit 27 1 14 https://doi.org/10.1002/stc.2501
doi: 10.1002/stc.2501
F Li Z Wang G Liu C Fu J Wang 2015 Hydrostatic seasonal state model for monitoring data analysis of concrete dams Struct Infrastruct Eng 11 1616 1631 https://doi.org/10.1080/15732479.2014.983528
doi: 10.1080/15732479.2014.983528
J Mata 2011 Interpretation of concrete dam behaviour with artificial neural network and multiple linear regression models Eng Struct 33 903 910 https://doi.org/10.1016/j.engstruct.2010.12.011
doi: 10.1016/j.engstruct.2010.12.011
Mathieson WL, Croft P, Wuttig F (2020) Influence of anisotropic permeability on slope stability analysis of an earthen dam during rapid drawdown. Geo-Congress 2020 Eng Monit Manag Geotech Infrastruct 289–298. https://doi.org/10.1061/9780784482797.004
Moriasi DN, Arnold JG, Van Liew MW, Bingner RL, Harmel RD, Veith TL (2007) Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. Trans. ASABE 50, 885–900. https://doi.org/10.13031/2013.23153
JE Nash JV Sutcliffe 1970 River flow forecasting through conceptual models part I—a discussion of principles J Hydrol 10 282 290
doi: 10.1016/0022-1694(70)90255-6
L Pagano E Fontanella S Sica A Desideri 2010 Pore water pressure measurements in the interpretation of the hydraulic behaviour of two earth dams Soils Found 50 295 307 https://doi.org/10.3208/sandf.50.295
doi: 10.3208/sandf.50.295
TV Panthulu C Krishnaiah JM Shirke 2001 Detection of seepage paths in earth dams using self-potential and electrical resistivity methods Eng Geol 59 281 295 https://doi.org/10.1016/S0013-7952(00)00082-X
doi: 10.1016/S0013-7952(00)00082-X
T Qin H Wang G Wang Y Liu X Li 2017 Heterogeneous influence on hydro-thermal behaviors within the core of an embankment Dam Geotech Geol Eng 35 2277 2290 https://doi.org/10.1007/s10706-017-0243-7
doi: 10.1007/s10706-017-0243-7
V Ranković N Grujović D Divac N Milivojević 2014 Development of support vector regression identification model for prediction of dam structural behaviour Struct Saf 48 33 39 https://doi.org/10.1016/j.strusafe.2014.02.004
doi: 10.1016/j.strusafe.2014.02.004
Rehamnia I, Benlaoukli B, Jamei M, Karbasi M, Malik A (2021) Simulation of seepage flow through embankment dam by using a novel extended Kalman filter based neural network paradigm: case study of Fontaine Gazelles dam, Algeria. Meas J Int MeasConfed 176. https://doi.org/10.1016/j.measurement.2021.109219
R Rosipal LJ Trejo 2001 Kernel partial least squares regression in reproducing kernel Hilbert space J Mach Learn Res 2 97 123 https://doi.org/10.1162/15324430260185556
doi: 10.1162/15324430260185556
F Salazar R Morán M Toledo E Oñate 2017 Data-based models for the prediction of dam behaviour: a review and some methodological considerations Arch Comput Methods Eng 24 1 39 https://doi.org/10.1007/s11831-015-9157-9
doi: 10.1007/s11831-015-9157-9
F Salazar MA Toledo E Oñate R Morán 2015 An empirical comparison of machine learning techniques for dam behaviour modelling Struct Saf 56 9 17 https://doi.org/10.1016/j.strusafe.2015.05.001
doi: 10.1016/j.strusafe.2015.05.001
RE Schapire 1999 A brief introduction to boosting IJCAI Int Jt Conf Artif Intell 2 1401 1406
SFOE (2015) DamBASE user manual: dam behaviour analysis software environment
S Sica L Pagano F Rotili 2019 Rapid drawdown on earth dam stability after a strong earthquake Comput Geotech 116 103187 https://doi.org/10.1016/j.compgeo.2019.103187
doi: 10.1016/j.compgeo.2019.103187
Simon A, Royer M, Mauris F, Fabre J (2013) Analysis and interpretation of dam measurements using artificial neural networks, in: Proceedings of the 9th ICOLD European Club Symposium, Venice, Italy
P Talukdar A Dey 2019 Hydraulic failures of earthen dams and embankments Innov Infrastruct Solut 4 https://doi.org/10.1007/s41062-019-0229-9
D Tang B Gordan M Koopialipoor DJ Armaghani R Tarinejad BT Pham V. Van Huynh 2020 Seepage analysis in short embankments using developing a metaheuristic method based on governing equations Appl Sci 10 1 23 https://doi.org/10.3390/app10051761
doi: 10.3390/app10051761
Vapnik VN (1995) The nature of statistical learning. Theory
Sw Wang Yl Xu Cs Gu Tf Bao 2018 Monitoring models for base flow effect and daily variation of dam seepage elements considering time lag effect Water Sci Eng 11 344 354 https://doi.org/10.1016/j.wse.2018.12.004
doi: 10.1016/j.wse.2018.12.004
Willm G, Beaujoint N (1967) Les méthodes de surveillance des barrages au service de la production hydraulique d’Electricité de France, problèmes anciens et solutions nouvelles, in: IXth International Congress on Large Dams. pp. 529–550
Y Xiang Sy Fu K Zhu H Yuan Zy Fang 2017 Seepage safety monitoring model for an earth rock dam under influence of high-impact typhoons based on particle swarm optimization algorithm Water Sci Eng 10 70 77 https://doi.org/10.1016/j.wse.2017.03.005
doi: 10.1016/j.wse.2017.03.005
C Xu C Deng 2011 Solving multicollinearity in dam regression model using TSVD Geo-Spatial Inf Sci 14 230 234 https://doi.org/10.1007/s11806-011-0527-7
doi: 10.1007/s11806-011-0527-7
H Yoon SC Jun Y Hyun GO Bae KK Lee 2011 A comparative study of artificial neural networks and support vector machines for predicting groundwater levels in a coastal aquifer J Hydrol 396 128 138 https://doi.org/10.1016/j.jhydrol.2010.11.002
doi: 10.1016/j.jhydrol.2010.11.002
Y Yu W Li J Li TN Nguyen 2018 A novel optimised self-learning method for compressive strength prediction of high performance concrete Constr Build Mater 184 229 247 https://doi.org/10.1016/j.conbuildmat.2018.06.219
doi: 10.1016/j.conbuildmat.2018.06.219
Y Yu Y Li J Li X Gu 2016 Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator Neurocomputing 211 41 52 https://doi.org/10.1016/j.neucom.2016.02.074
doi: 10.1016/j.neucom.2016.02.074
Y Yu TN Nguyen J Li LFM Sanchez A Nguyen 2021 Predicting elastic modulus degradation of alkali silica reaction affected concrete using soft computing techniques: a comparative study Constr Build Mater 274 https://doi.org/10.1016/j.conbuildmat.2020.122024
Y Yu C Zhang X Gu Y Cui 2019 Expansion prediction of alkali aggregate reactivity-affected concrete structures using a hybrid soft computing method Neural Comput Appl 31 8641 8660 https://doi.org/10.1007/s00521-018-3679-7
doi: 10.1007/s00521-018-3679-7
A Zewdu 2020 Modeling the slope of embankment dam during static and dynamic stability analysis: a case study of Koga dam, Ethiopia Model Earth Syst Environ 6 1963 1979 https://doi.org/10.1007/s40808-020-00832-8
doi: 10.1007/s40808-020-00832-8